The consumption of energy in the form of electricity is a modern facet of modern living. However, the production of energy often requires the activation of large turbine generators that convert mechanical energy into electrical energy. This mechanical energy is typically created by moving water, steam, and/or gas across the blades of the turbine thereby causing them to revolve, these revolutions then in turn cause a giant magnet to turn, which in turn creates a magnetic field that causes electrons in an associated electrical circuit to flow. Such flow is termed “electricity.” The energy that creates the steam or gas that flows across the blades of the turbines is, from a historic perspective, usually generated by the burning of fossil fuels, such as coal, oil, and/or natural gas. Unfortunately, when a fossil fuel is used to run the turbines, such as coal, natural gas, oil or the like, pollution, in the form of carbon emissions, may be produced, which may cause deleterious environmental conditions. Accordingly, renewable resources are now beginning to be deployed on a more wide scale basis for the production of electricity.
For instance, electricity may be produced by the running of water over the blades of the turbine, such as at a hydroelectric plant, and/or may be produced by nuclear energy, solar power, or wind power. However, for wide scale use purposes, such energy producing facilities require large physical plants and/or farms of photovoltaic cells or fields of wind turbines. Because of the need for large physical facilities and the undesirable polluting side effects of producing energy, e.g., by the burning of fossil fuels, the power plants that generate such electricity are often located in places that are remote from the residential neighborhoods that ultimately use the produced electricity. Consequently, the energy produced by such power plants needs to be transferred, such as through a transmission network, from the remote locations of production to the site of usage by the ultimate consumer. This transmission of electricity is typically carried out across a network of thick wires that connect the power generation source to the consumer where such network is commonly referred to as the “electric grid”.
The electric grid or “grid” is a network for transmitting electricity from a producer and/or supplier ultimately to a consumer. Hence, the grid is interconnected on the generation side with power suppliers, on the distribution side with centralized power distributers, and on the use side with consumers, the collection of which forms one or more “Macro Grids”. Most consumers of electricity are grid tied, which simply put means being connected to the macro grid for electricity use. This is primarily due to the fact that the most stable power source to date, in modern cities is the electrical grid. However, with the rapid adoption of renewable resource generation, specifically on the consumer side of the grid, this may, with the right technological advancements, change drastically.
The macro grid, therefore, generally includes a plurality of centralized generation sources, a number of distribution centers, and the infrastructures necessary to provide electricity to the consumer including the various transmission lines necessary for such electricity transfer. The remote generation source is typically where electricity is produced and packaged into a usable form, such as in a form suitable for transmission. For instance, transmission from the remote areas of production, to the far away areas where it will finally be used.
For example, dependent on the type of generator employed and the generation process, the electricity produced will either be in the form of an alternating current (AC) or a direct current (DC). Yet, because DC does not travel well over long distances, in those instances where DC is produced, it is typically converted to a form of AC prior to transmission. More particularly, dependent on how the grid is constructed, the electricity produced will be transmitted at a given voltage having a specific frequency so as to deliver a certain electric current, such as to the distribution center. More particularly, when such electricity is travelling on the transmission side it may range from about 1,000 kV or about 800 kV or about 765 kV to about 300 kV or about 115 kV, etc. Accordingly, this side of the macro grid is generally referred to as the transmission grid.
The transmission infrastructure typically includes large capacity, high voltage power lines that act as an electricity super highway for transferring energy from the remote locations of its production to the populated areas of its usage; and/or may further include one or more transformers that step the electricity that passes through it up or down so as to be efficiently transmitted and/or used. For instance, once produced the voltage of the current may be stepped up so as to maximize the speed and quantity of energy transmission, while reducing the size of the wiring through which the electricity is transferred and/or reducing the thermal heat generated by such transmission.
Distribution Substations are typically where the electricity is received and stepped down via one or more transformers so as to decrease the voltage and frequency of the current to a level suitable for transmission to the consumer, which upon delivery to a local transformer servicing a given area of consumers may be stepped down once more to a final level that may be used. This side of the macro grid is usually referred to as the distribution grid. More particularly, when such electricity is travelling on the distribution side it may range from about 200 kV or about 132 kV or about 33 kV to about 25 kV or about 3.3 kV, etc. Once stepped down, local distribution lines deliver the electricity to the consumer where, as indicated, the electricity may be stepped down an additional time, such as to 110-240 volts (such as at about 50 or about 60 cycles) so as to be in a form usable by the consumer. On the consumer side of the grid, such electricity usually enters the consumer's place of use through a meter that measures the amount of electricity used, and in a manner such as this theoretically reliable, stable electricity may be generated and distributed to the end use customer via the electric grid.
The macro grid, therefore, is configured for producing, transmitting, and distributing electricity to the ultimate consumer and user, such as upon demand. Simply put, however, this macro grid is a legacy grid, and as such it is built on an archaic infrastructure, using outdated transmission lines, and with insufficient control mechanisms for handling the complex usage scenarios that result from its diverse local customers, thus severely limiting its ability to meet the ever-increasing demands of the consumer in a cost-effective and environmentally responsible manner. More specifically, this legacy macro grid is basically not configured so as to efficiently deal with the fluctuating usage demands of the consumer and has long been struggling with maintaining stability in the face of such fluctuating demand.
For instance, as consumer demand curves differ with the differing needs of the various customers served by a particular macro grid, the supply curves representing the ability of the respective power generators and/or distributors to meet those needs must also fluctuate. This difference between the demand and supply curves represents a huge problem for the power generators, distributors, and ultimately for the consumers, but also for the electrical utility investors and regulators.
For example, the response to increased energy demand appears on the grid as peaks, the greater the amplitude and frequencies of these peaks, the greater the potential for destabilization in the grid to occur, thus creating problems ranging from overloaded transformers to brown and/or blackouts, such as when overloaded transformers completely shut down. More particularly, once overloaded, transformers experience increased wear and reduced operational life, thereby requiring higher maintenance, and increasing their likely hood of shutting down during the next period of inordinately increased demand, thereby causing a brown and/or blackout condition. Decreased demand can also be problematic. For instance, low demand appears on the grid as valleys. For example, in a low demand scenario, power suppliers are faced with having too much energy flowing across the grid, requiring the power suppliers to have to dump the excess power to keep from crashing the system.
Accordingly, any fluctuation in the legacy grid may cause general instability for the grid operator(s) thereby potentially causing problems with the power generators, such as not running at optimal usage levels, and/or problems with the transformers, which in turn may result in one or more of flow inefficiencies; transformation inefficiencies (such as where energy undergoes too many or too few conversions); waste, such as through leakage, radiant heating, or being converted from one form to another; inefficient coupling; overproduction; under production; and the like. And when these instabilities increase, entire grid shutdown may be threatened.
Hence, in view of the multiplicity of problems constantly threatening to shut one or more portions of the macro grid down, a central regulator is needed to facilitate communication and develop protocols to maintain a more stable grid. For instance, a typical distribution center includes a governor that monitors an electronic representation of the grid with respect to the present demand and supply curves. For example, in a typical scenario, where demand outweighs supply, the monitor must balance the need for producing more power, with the risk of both producing too much power, and therefore creating waste, and not producing enough power, and thus risking a brown and/or black out. In such an instance, where the monitor determines more energy should be supplied to the grid, it may be determined that an auxiliary production facility, such as a peaker plant, need be brought on line.
A peaker plant is an energy production facility, e.g., a sub-station, which houses one or more generators. These generators are simply waiting to go live, so they can be ramped up, be quickly brought on line to meet the increased supply demand, and thereby prevent potential brownout situations caused by under capacity. Peaker plants, however, can be problematic in their own right. For instance, a typical peaker plant costs an exorbitant amount of money to produce, must be built in accordance with strict regulations, and once up and running is always running, e.g., at a basal level, thus, generating waste when not online. More particularly, peaker plants sit idle in anticipation of the next energy peak caused by consumer usage demand, and while sitting idle produce unnecessary emissions due to this “always on” scenario wherein fossil fuel is constantly being burned and its emissions released into the atmosphere.
As indicated, peaker plants require a high installation cost, and must undergo a lengthy regulatory process before a new facility may be approved, built, and brought online. Further, even when approved such plants often become obsolete prematurely due to changes in regulatory mandates. Thus, a huge problem for the energy supplier and/or investor is the fact that the cost of this asset is largely never recouped. There is a constant battle, therefore, between supplying too much energy to the grid and not enough energy.
In order to better manage the issues of grid instabilities caused by inconsistent and fluctuating user demand, as well as minimize the need for wide scale usage of peaker plants, energy supply companies have developed a number of different schemes directed at changing the electricity use patterns of the consumer, a main component of which is through various different pricing modalities. However, there a several problems inherent with the various pricing modalities proposed, not the least of which is the fact that the existing electrical grid is only configured for transmitting electricity as if it were a commodity rather than a renewable resource. More specifically, grid operators have the difficult task of determining how to charge consumers for the product, e.g., electricity, and/or services they provide.
To date, the electricity distributor typically charges the electricity consumer based on the over all usage patterns of the collective of consumers. Hence, the individual consumer is charged a higher rate at peak times of demand, than the rate they are charged during off peak times, thus, making the electricity product more of a commodity, having a limited supply, rather than a service, such as cable or internet. As mentioned above, historically, electricity generation has been produced from fossil fuel sources such as coal and natural gas, thus to a limited extent justifying the treatment of electricity like a commodity. However, with the shift to electricity production from renewable energy resources, such as photovoltaic and/or wind farms, as well as the development of hydroelectricity, the correlation of electricity to a limited resources, e.g., a commodity, is becoming more and more of a stretch.
The problem with this commodity-type of pricing is even more exacerbated when the electricity distribution company attempts to change the use patterns of their customers by adopting various different pricing modalities for the sole purpose of changing the consumer's usage behaviors. For instance, as a means of changing the consumer's behavior various utility companies have proposed a range of different pricing models, such as “Time of Use Pricing”, “Dynamic Pricing”, and/or “Demand Response Pricing.” These and other such pricing models are in concept designed to give the consumers various use options in hopes of creating a behavioral change that will mainly benefit the electricity distributor.
For example, “Time of Use” pricing was initially designed to incentivize commercial energy customers to reduce peak-time usage by increasing utility rates during peak-demand periods, and reducing pricing outside normal, non-peak-demand usage, in an effort to help smooth out grid fluctuation cycles. Time of Use pricing, however, is confusing to the customer, in part because their various different associated rates now have several different pricing categories for the same commodity being purchased, where price fluctuation depends simply on what time of day that commodity is being consumed and/or for what the commodity is being used and/or who it is that is doing the consuming.
More particularly, if the consumer is a commercial user or a residential user having solar power or owning an electric car, such consumers will have different “peak demand” pricing windows than the typical residential user, even though they are consuming the same energy at the same time. For instance, those who have solar power connections have a “peak demand” pricing period that begins in the evening, rather than during the day, merely because they have a solar generator connected to the grid, despite the fact that they are using the same electricity provided to them at the same time period as any other residential user with the only difference being that during daylight hours, the residential user with solar power does not typically need to use power from the grid. Nevertheless, in order to maintain a certain level of return on investment, the utility provider shifts the “peak demand” pricing period for such users to the non-daylight hours thereby charging them more at night than during the day, in contravention to the rate being charged to the typical residential consumer who does not have solar power.
With respect to “demand response” pricing, this pricing model is a grid management technique where retail or wholesale customers are requested either electronically or manually to reduce their load. Currently, transmission grid operators, e.g., power distribution companies, use demand response to request load reduction from major energy users such as industrial plants. More particularly, demand response pricing involves energy pricing that follows the intermittent consumer demand on the electrical grid, which requires consumers to follow energy pricing, prior to use. Essentially the Distributed Services Organization, e.g., the Utility, will monitor usage and at various times of the day when demand begins to peak above supply, they will make an announcement in real time to warn consumers of a hike in the pricing of use. They expect such pricing events to occur daily, where each day there could be several such events.
Unfortunately, these complex pricing programs have proven to not be as effective as hoped. For instance, the desired outcome was to reduce peak time use in order to help stabilize grid operation. However, in order to be successful, these programs depended on the consumers understanding and/or caring about grid issues enough to ultimately change their behaviors at the arbitrary use-times demanded from the utility providers; and further these programs were based on punishing the “bad behavior” of the consumer by making them pay more for electricity usage if they did not adhere to the usage periods arbitrarily determined by the Utilities.
More particularly, these pricing models are founded on the expectation that consumers will change their routines or suffer the consequences of higher energy pricing if they don't. Further, the reward for giving in to the demands of the utility providers is not being able to access the grid at times when most needed, e.g., during days of high temperatures, or nights of low temperatures. Consumers simply do not want to deal with these inconveniences.
Furthermore, for the commercial consumer, such as product manufacturers, these consumers were expected to shift their production efforts to “off-peak” times that typically do not coincide with regular hours of operation, simply to move energy usage times to suit the energy supplier's, e.g., DSO's, needs. This is especially problematic for those manufactures that need to operate their equipment consistently 24/7 with no ability to shift loads to off peak times. Hence, for the commercial consumer, these programs require them to pay attention to their usage times and to make some very difficult decisions as to how and when to use their equipment.
Other programs that have been developed and implemented by the grid operators to better manage the electricity use patterns of the consumer involve communications media. For instance, the utility provider as a further means for changing the user's behaviors employs communications media. Such media have included the use of in home displays (IHD) or grid-tied I demand response thermostats, coupled with energy monitoring devices. These IHDs are consumer facing energy display/monitors that connect either in a wired or wireless configuration with a smart meter to show electricity usage to the consumer. The principle behind the use of such IHDs is to change the consumer's behavior by making the interaction with usage easy and commonplace. More particularly, the idea was to help consumers better understand and relate usage costs to peak times of demand where such peaks are determined by historic usage models.
For instance, one such monitoring device is a grid tied demand response thermostat. In use, a customer will opt in to the program, the utility company will install the demand response thermostat, then the utility company will control the thermostat, and in times of peak demand will set it back thereby preventing its use. These and other types of electrical load curtailment devices on the customer side of the meter increases the Distributed Services Organization's ability to stabilize the grid. However, these devices offer complicated options to an already complicated issue and have yet to offer any significant long-term value, plus customers don't like having the Distributed Services Organization turn off their appliances, e.g., air conditioning, without any way to override this decision.
Other options, beyond the mere implementation of price regulations and/or transmission of media communications, have been proposed for solving the problems of fluctuations caused by peak time usage demand. For instance, the production of grid-side solar farms and wind farms, as well as consumer side solar energy generation, have been developed to help assuage the problem of fluctuating consumer side electricity use of their local portion of their macro grid. However, although these renewable energy modalities were expected to help stabilize the grid by generating power that would offset peak demand, in actuality, there are several problems inherent to these proposed means of energy production that renders their effectiveness de minimis.
For instance, an issue with renewable resource power generation, regardless of the side of the grid they reside upon, is due to the non-linear and intermittent nature of the natural environment. For example, when the sun is shining solar energy is capable of being produced. But, when clouds cover the sun, or the sun is otherwise not shining, solar energy is not readily producible. The same can be said for the production of energy from wind. When it is windy out, energy is capable of being produced, but when it is not windy out, energy cannot be produced from a wind farm. The problem with such intermittent energy production is that it is always in a state of flux. This is a significant issue for both the power generator and the Distributed Services Organization.
Currently, energy produced by renewable resources, such as on the utility side of the grid, may be added on to the grid in a particular, predetermined quantum and at a predetermined time. In instances where too much power is being generated and/or at times when the grid cannot accommodate that energy, such as without becoming destabilized, the renewable resource power generator will be required to disconnect from the grid and/or otherwise discharge the generated energy, thereby creating waste. Simply put, the grid is just not configured for efficiently dealing with the excessive generational spikes, such as above the established median line (manageable standard set by the operator), which occurs from renewable resource energy production and/or energy production on the consumer side of the grid.
Additionally, distributed energy production resources, such as rooftop solar and/or wind turbine generation on the customer side of the meter, and/or other sources of local generation, have proven problematic for the legacy grid to handle. For instance, on the consumer side of the grid, the grid operator currently does not have a way to track, direct, and/or otherwise control the electricity being produced and shoved back onto the grid from the consumer side of renewable resource power production. More particularly, the traditional grid was not designed to accommodate a bidirectional flow of electricity. With the growing number of renewable resource power generation systems, such as being installed on the consumer side of the grid, ever increasing amounts of power is now being attempted to be supplied to the grid from the consumer, where such over generation of power instead of helping to smooth out the demand curve is actually destabilizing the grid.
Such destabilization makes the grid unmanageable by Distributed Services Organizations that other than price regulation lack proper controls beyond the meter to handle the fluctuations due to consumer side power production. This is largely due to the fact that the legacy grid does not allow for real time information related to consumer side power production to be relayed to and from the grid, which is made even more problematic in view of the uptrend and adoption of consumer side generation. Consequently, on the customer side, local meter-side energy production creates its own problems in that any excess energy produced on the consumer side usually has to be shoved back on to the grid and stored thereon thus utilizing the grid as a large battery, yet the grid was never designed to function in this manner.
For example, Distributed Energy Resources (DERs), such as distributed energy generators, smart meters, and the like) requires the electrical grid to act as a battery storage facility by which the customer can call on that power when needed. In some areas, the Distributed Services Organization (DSO) cannot accept any more generation, having to refuse customers that want to install grid tied, personal use solar panels. This is problematic for everyone involved, especially in those instances where the utility company has to pay consumers “not” to install solar panels and/or wind turbines. Hence, consumer side power generation has created a new problem of bidirectional flow.
Centralized battery storage has been introduced on the utility side of the grid, to help compensate for the intermittent nature of commercial renewable resource energy production, as well as in those instances where energy production, such as during non-peak time energy generation at a peaker plant exceeds that used by the consumer. For instance, commonly, where the over production of energy occurs, that energy is typically wasted. Centralized, grid-size battery storage has been advanced as a possible solution to this problem. More particularly, grid side, centralized battery storage is an attempt to mimic traditional gas fired peaker plants.
However, this model is very inefficient for batteries, due to the fact that the battery storage resides on the utility side of the meter. Such centralized battery storage only allows the DSO to react to demand events, it does nothing to address the bidirectional flow from customer side Distributed Energy Resources. Further, such batteries store electricity at an overall loss due to conversion from AC (transmission) to DC (storage) and back again. This loss is increased when transmission is also part of the equation.
In view of the above, it is clear that a major problem with the macro grid, to date, is that it remains largely unintelligent, and thus, the modern changes in both usage and generation are causing the grid to become more and more unstable, resulting in an increased risk of grid outages. These problems become even more complicated as the macro grid is expected to grow and grow into a super grid. For instance, with the realization of long distance power transmission, such as from the power producer to the power distributer, on the transmission grid, and/or from the power distributor to the consumer, on the distribution grid, it has become possible, at least theoretically, to interconnect different centralized distribution centers with far ranging power generation stations in the hope of being able to more effectively balance loads and improve load factors and/or create a nation wide grid.
However, in order to implement a nation wide grid, power production and transmission needs to be synchronous. For instance, power generation and distribution centers on a city, county, state, and/or nationwide basis may be configured so as to form a synchronous group of production and distribution areas, which if configured correctly may all operate with synchronized alternating current frequencies so that the peaks and troughs of the electricity flows occur at the same time). This allows transmission of AC electricity throughout the area, connecting a large number of electricity generators and/or distribution centers and/or consumers and potentially enabling more efficient electricity markets and redundant generation. For instance, a typical synchronized AC grid, can be configured so as to be running at 132 kilovolts and 50 Hertz.
It was hoped that such networked interconnectivity would convert the macro grid into larger and larger versions of the grid that would be state, nation, or even continent wide. There have been several proposals for how such larger grids could be implemented, however, according to the proposed plans, to do so would expectedly require a dramatic increase in transmission capacity, fine tuned internal control, as well as a synchronized global communications protocol. All of these would require a huge outlay of financial resources possibly escalating into the billions of dollars range.
The benefits of such a nation or even continent wide grid are compelling and include enabling the energy production industry to sell electricity to distant markets, thereby increasing competition, the ability to increase usage of intermittent energy sources by balancing them across vast geological regions, and the removal of congestion and commodity like billing structures that prevents electricity markets from flourishing. However, in order for such large-scale grids to be implemented, some major hurdles must be overcome. For instance, its implementation faces local opposition to the siting of new lines and building out the necessary physical infrastructure, there are significant upfront cost to these projects, and there are major difficulties inherent in managing the energy flow and communications necessary for enabling a true county, state, or even nationwide grid.
Further, a necessary component of such a large grid that is yet to be developed and adopted, therefore, is a sufficient management system that is capable of multi-county, multi-state, nationwide and/or continent wide communication as well as grid management on all of the power generation, distribution, and consumer consumption sides of the grid. A macro grid management system is the subsystem of the electric grid that provides management and control services to the macro grid. It requires a huge infrastructure that is controlled and run by massive computer banks, in response to a multiplicity of grid related monitors and sensors, as well as in response to the totality of individual usage scenarios.
Typically, these management systems are run in isolation of one another on a county by county, state by state basis making inter connectivity and overall grid management extremely difficult, if not impossible. For instance, as the macro grid expands into becoming a mega grid, such as by attempting to provide service to ever increasing areas of demand, the various different, respective electric macro grids will need to be configured so as to run synchronously, and consequently, they will need to be able to communicate and interact with one another. More particularly, in a large-scale, maximally efficient synchronous super grid, various different power generators should be configured to run not only at the same frequency but also in the same phase, such as where each generator is maintained by a local governor that regulates the driving torque, for instance, by controlling the steam supply to the turbine driving it.
However, maintaining such synchronicity can be problematic. For instance, in an efficient grid energy should be consumed almost instantaneously as it is produced, generation and consumption, therefore, should be balanced across the entire macro, mega, and/or super grid. Consequently, the grid management system needs to be closely controlled to mirror the demand curve with the supply curve.
For example, demand is the usage of electricity, e.g., the drawing of electricity from the grid by the consumer, where the demand curve is due to the ever-fluctuating usage by the collective of serviced consumers at any given point in time. Thus, demand curves differ from location to location, and from time to time. Supply, on the other hand, is the provision of electricity to the grid, where the supply curve is due to the throttling up or down of power generation, e.g., of fossil fuel or renewable resource power generation, in a manner to meet the fluctuating usage of the demand curve. This becomes problematic as the size of the grid servicing a multiplicity of communities increases, because the task of matching the supply curve to the demand curve becomes increasingly more complicated and difficult. In such situations, the management system is under constant pressure as it tries to find and maintain a balance that is equal between generation and need.
More specifically, over capacity (excessive generation) as well as under capacity (greater demand than supply) creates an unstable electrical grid. And both situations can lead to power outages. Particularly, a large failure in one part of the grid, unless quickly compensated for, can cause current to re-route itself to flow from the remaining generators to consumers over transmission lines of insufficient capacity to handle the extent of the travel, causing further failures, which failures if left unchecked can lead to a cascading shutdown. Hence, a huge downside to a widely connected and/or synchronous macro grid is thus the increased possibility of cascading failure and widespread power outage.
More particularly, the more complex the grid becomes the greater the potential for brown and/or black outs. Accordingly, in order to be fully operational on an international, national, state, or even on a county wide basis, electronic circuitry required for running, managing, and controlling the electric grid, e.g., a universal grid management system, must be constructed, which requires extensive research and development.
Additionally, such an international and/or nation wide gird would require enormous upfront costs for the land, generators, computers, and equipment, as well as demanding a large amount of manpower to build and run the necessary infrastructure. More particularly, in order for such a universal management system to be run efficiently it would need to be smart. So being, in order to be smart, it would also need to be energy efficient, and all of its supply and demand profiles, utility configurations, cost models, and emission standards would need to be improved, such as by optimizing and building out the local infrastructures and control mechanisms.
For instance, within the advanced infrastructure framework of a smart grid, more and more new management services and software applications are needed to emerge so as to eventually revolutionize the macro grid and enhance the consumers' daily lives. However, to date, the legacy grid does not have a management system or the physical infrastructure that is capable of adequately dealing with the ever-increasing demand fluctuations of a consumer base that is rapidly growing. Further, the current macro grid is simply not set up to deal with the inconsistencies of solar and/or wind supply, in addition to the vulgarities of intermittent usage. Accordingly, the present macro grid needs to be updated, e.g., it needs to become intelligent or smart, so that it can deal with an increasing amount of inconsistent demand as well as generation.
What is needed, and presented herein, therefore is a bottom up solution that can revolutionize the way the legacy grid functions, without necessarily having to completely rebuild the entirety of existing local, regional, and/or macro grid networks.